{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Sparse Kernel Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from prml.kernel import (\n",
    "    RBF,\n",
    "    PolynomialKernel,\n",
    "    SupportVectorClassifier,\n",
    "    RelevanceVectorRegressor,\n",
    "    RelevanceVectorClassifier\n",
    ")\n",
    "\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Maximum Margin Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9x6a/zb+98C0FvhVIozNG06dFn1LKhynkH+hP9/nfR8Hzgun09dNUVlZGkyZN\nojlz5tj0/Wprax22h72c8YjAPPC1A+6nqrYKm49vRmZRJipuVaDdnXbw+sILuVtzMX36dKxZswYA\noNVqoVKpoNFooFKpzI4Krl+/jk2bNqGkpAQjR47E0KFDXf3HcanS0lJERkairKyM72tQx55rBxwy\nJ2DvocSRwL3Q6XS0e/duCg0NpS5dutDFixeJiOjWrVvG55i7nLSyspImTJhAISEh1KJFCxo/frxx\nDsJTmXYW8vKhfSMBDgE3UFZWRpmZmcbHU6ZMoQULFhgfNzXs37FjB/Xs2ZOCgoKouLjY6XVKjfsI\n7rInBHhi0A34+/tj3LhxAACNRoMXX3wRGRkZCAkJwYEDB+DlVf9/I9Wd4h0/fhwajQaLFy9GaGio\n8fOeipcP7w2HgJtp0aIFXnjhBeTl5WHIkCF466238Msvvxi/rtPpIIRAdnY2tm7din79+mHWrFkS\nVuxafBmy/TgE3FRgYCA2btyIjRs3ol27dgD0AeDl5YXy8nKsXbsWRAS1Wm1sMlLK0iIvH9qHQ8CN\nCSHw4IMP6q86JDKeFqxduxY//PADJkyYgAEDBgAAVCqVlKW6nCEI+OpD6zgEPITht/yBAwewbds2\nPP7445g7dy4A/QhBifgyZNtwCHiAM2fOAABqamqwdu1a1NTUYP78+fD19TWeIigVB4F1yv3p8BDf\nf/89nnjiCaSkpCA1NRUFBQUYN24cnn766XqnCErG+xFYYetaoiMP7hNwrIULF5Kvry8JIWjgwIF0\n6dIlqUuSJSX1EYD7BJSB6tb9ly9fjpMnT2Lo0KE4cuQIVq1aZewRYHdxH4F5HAJuzLAqoNVq8dBD\nD2Hv3r1ISUnB8uXLMXr0aL7lmRkcBI1xCLg5IQRUKpVxs5HY2FhUVFRg1qxZyMrKQkxMDAdBA9xQ\nVB+HgIdQqVTGUUHr1q0RHx+PlJQUZGZmchCYwQ1Fd3EIeBDDqMAgPj4eqampyMzMhFqtlrAyeeKG\nIr0WUhfAnEutVqNNmzaIioqSuhRZMiwfRkZGIioqSpH7EfBIQAFmzpyJbt26QafTYf369Xxq0IDS\nG4o4BBRk3759mDZtGq8amNGwoUhJqwYcAgoSFRWFlJQUZGVlcRCYodTlQw4BhTGsGnAQmKfEIOAQ\nUCBDEGzduhV5eXlSlyM7SgsC3m1YwU6ePInQ0FCpy5Ct0tJSREREoLy83O1WDezZbZhHAgpmCIDt\n27fj5ZetfIG0AAAHiklEQVRf5lODBpTSUMQhwHD69Gl8/PHH3FlohhIaijgEGObPn2/sLOQgaMzT\n+wg4BBgAfWchB0HTPHljEg4BZmQIAl9fX8XsTGyPhnMEnrJq0KzVASFEKoCRAGoBnAbwJyKqsPY6\nXh2QNyKCEAJXrlxB+/bt0aIFX2JiynTVICcnBwMHDpS6pEZcuTqwF0BvIuoL4BSARc18PyYDQghU\nVVVh8ODB3FBkhqf1ETQrBIgoh4gMPyF5AIKaXxKTg9atW/PGJBaYrhq4+8YkjpwTmApgtwPfj0mM\nJwst85RVA6shIIT4jxCi0MwxyuQ5SwBoAGy28D4zhBAFQoiCsrIyx1TPnM40CBYt4rO9hjwhCJrd\nNiyEmAJgJoChRFRty2t4YtD9rF27Fs8//zw6d+4sdSmyJLcWY5dNDAohhgNYAOB3tgYAc0/Tp09H\n586dodFosGrVKj41aMCdlw+bOyfwPoA2APYKIY4KIVY7oCYmY9nZ2Zg9ezavGpjhrqsGzV0deJSI\ngomoX90xy1GFMXkaMWIE70dggTsGAXcMMrvxxiSWudt9DTgE2D0xBMFnn32G77//XupyZMedLkPm\nEGD3LD4+HidOnMCAAQMA3L03ItNzl8uQOQRYs3Tr1g0AsGnTJm4oMsMd+gg4BJhDXLlyBVu2bOEg\nMEPuQcAhwBwiLi6O731ogZz3I+AQYA5jehNUXjVoTK4NRRwCzKEMQdChQ4d6N0dlenLsI+Atx5lT\nGDYmuXDhAjp16sQbkzTg7I1JeMtxJjkhBG7cuIFBgwbxHIEZcmoo4hBgTuPn54d58+bxZGET5NJQ\nxCHAnIo3JrFMDg1FHALM6UyDIDExUepyZEfqPgKerWEuoVar4e/vjxEjRkhdiiwZgiAyMhJRUVEu\n3ZiERwLMZaZMmQJ/f3/U1tbi7bff5lODBho2FLlq+ZBDgLnc559/jri4OG4oMkOKPgIOAeZyo0eP\n5v0ILHB1EHAIMEnwxiSWuTIIOASYZOLj45GamoqdO3fixx9/lLoc2XFVQxG3DTPJXbhwAUFB+ptX\nGdqN2V33sp05tw0zt2IIgLS0NEycOJFPDRpwdkMRhwCTjZs3byIjI4PnCMxo2FDkyDkCDgEmG4bO\nQp4sNM+0j8CRk4UcAkxW1Gp1vVUDrVYrdUmy4oxVA24bZrITHx8PAKioqICXF/+easgQBBEREYiO\njm72fgS8OsBk78yZMwgODuaNSRqwtDEJrw4wj3Ht2jUMHDiQ5wjMMF01aE4fAYcAk7X27dtjwYIF\nPFnYBEdchtys8ZUQ4m8ARgHQAbgKYAoRXWrOezLWkFqtBnB3rmDz5s18amDC3GXI9mjuSCCViPoS\nUT8AOwEkNPP9GDPLdPnw73//u9TlyE7DEYE9mhWnRFRp8rAVAL4ZHXMatVqNwMBA3pikCaYjgsrK\nSusvqNPsMZUQYimAlwHcABBp4XkzAMyoe3hbCFHY3O/tQP4AyqUuwoTc6gHkVxPXY1morU+0ukQo\nhPgPgE5mvrSEiLabPG8RgPuIyOomckKIAluXL1yB67FObjVxPZbZU4/VkQAR/V8bv+9mALsA8E6S\njLmRZk0MCiEeM3k4CkBx88phjLlac+cE3hBChEK/RHgOwCwbX/dBM7+vo3E91smtJq7HMpvrkaRt\nmDEmH9wxyJjCcQgwpnCShYAQ4m9CiONCiKNCiBwhRGepaqmrJ1UIUVxX06dCiHYS1zNWCFEkhNAJ\nISRbehJCDBdCnBRC/CSEWChVHSb1rBdCXJVLn4kQIlgIkSuE+LHu/9dcieu5TwjxrRDiWF09yVZf\nRESSHADamnz8KoDVUtVSV0M0gBZ1H78J4E2J63kc+oaPAwDCJKpBBeA0gG4AfAAcA9BT4r+XpwE8\nCaBQyjpM6gkE8GTdx20AnJLy7wiAANC67mNvAPkABll6jWQjAZJZyzER5RCR4RK1PABBEtdzgohO\nSlkDgKcA/EREJURUCyAD+qVgyRDRFwCuS1mDKSK6TETf1X38K4ATALpIWA8RUVXdQ++6w+K/LUnn\nBIQQS4UQpQAmQV4XH00FsFvqImSgC4BSk8cXIOEPuNwJIUIAPAH9b18p61AJIY5Cf2XvXiKyWI9T\nQ0AI8R8hRKGZYxQAENESIgqGvttwjjNrsaWeuucsAaCpq0nyeph7EEK0BrAVwLwGo1yXIyIt6a/s\nDQLwlBCit6XnO/WibJJZy7G1eoQQUwCMADCU6k6qpKxHBi4CCDZ5HFT3OWZCCOENfQBsJqJtUtdj\nQEQVQohcAMMBNDmRKuXqgKxajoUQwwEsAPA7IqqWshYZOQzgMSHEw0IIHwAvAdghcU2yIvS3S1oH\n4AQRvS2DegIMK1tCiPsBRMHKvy3JOgaFEFuhn/02thwTkWS/ZYQQPwHwBXCt7lN5RGRrG7Qz6hkN\n4J8AAgBUADhKRMMkqON5AP+AfqVgPREtdXUNDer5HwAR0F+6ewVAIhGtk7CecABfAvgB+p9lAFhM\nRLskqqcvgHTo/395Acgiov9v8TVShQBjTB64Y5AxheMQYEzhOAQYUzgOAcYUjkOAMYXjEGBM4TgE\nGFO4/wWMjJECJCxHNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104476208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train = np.array([\n",
    "        [0., 2.],\n",
    "        [2., 0.],\n",
    "        [-1., -1.]])\n",
    "y_train = np.array([1., 1., -1.])\n",
    "\n",
    "model = SupportVectorClassifier(PolynomialKernel(degree=1))\n",
    "model.fit(x_train, y_train)\n",
    "x0, x1 = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))\n",
    "x = np.array([x0, x1]).reshape(2, -1).T\n",
    "plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\"x\")\n",
    "plt.scatter(model.X[:, 0], model.X[:, 1], s=100, facecolor=\"none\", edgecolor=\"g\")\n",
    "cp = plt.contour(x0, x1, model.distance(x).reshape(100, 100), np.array([-1, 0, 1]), colors=\"k\", linestyles=(\"dashed\", \"solid\", \"dashed\"))\n",
    "plt.clabel(cp, fmt='y=%.f', inline=True, fontsize=15)\n",
    "plt.xlim(-3, 3)\n",
    "plt.ylim(-3, 3)\n",
    "plt.gca().set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HH34gKCjIdQQHB9OgQQPOOussNE0jNTWV0NBQwsLCMJtP7YSc+9dc+jXvR9+W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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf81320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_toy_data():\n",
    "    x = np.random.uniform(-1, 1, 100).reshape(-1, 2)\n",
    "    y = x < 0\n",
    "    y = (y[:, 0] * y[:, 1]).astype(np.float)\n",
    "    return x, 1 - 2 * y\n",
    "\n",
    "x_train, y_train = create_toy_data()\n",
    "\n",
    "model = SupportVectorClassifier(RBF(np.ones(3)))\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "x0, x1 = np.meshgrid(np.linspace(-1, 1, 100), np.linspace(-1, 1, 100))\n",
    "x = np.array([x0, x1]).reshape(2, -1).T\n",
    "plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\"x\")\n",
    "plt.scatter(model.X[:, 0], model.X[:, 1], s=100, facecolor=\"none\", edgecolor=\"g\")\n",
    "plt.contour(\n",
    "    x0, x1, model.distance(x).reshape(100, 100),\n",
    "    np.arange(-1, 2), colors=\"k\", linestyles=(\"dashed\", \"solid\", \"dashed\"))\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.gca().set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1.1 Overlapping class distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H08bRhp6enjx6NHOe5xMPT9BzhieXnh1NnUFnLNdoNZx5pA8LzCrAgKcBdPvZ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044765f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_toy_data():\n",
    "    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\n",
    "    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\n",
    "    x = np.concatenate([x0, x1])\n",
    "    y = np.concatenate([-np.ones(50), np.ones(50)]).astype(np.int)\n",
    "    return x, y\n",
    "\n",
    "x_train, y_train = create_toy_data()\n",
    "\n",
    "model = SupportVectorClassifier(RBF(np.array([1., 0.5, 0.5])), C=1.)\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\n",
    "x = np.array([x0, x1]).reshape(2, -1).T\n",
    "plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\"x\")\n",
    "plt.scatter(model.X[:, 0], model.X[:, 1], s=100, facecolor=\"none\", edgecolor=\"g\")\n",
    "plt.contour(x0, x1, model.distance(x).reshape(100, 100), np.arange(-1, 2), colors=\"k\", linestyles=(\"dashed\", \"solid\", \"dashed\"))\n",
    "plt.xlim(-4, 4)\n",
    "plt.ylim(-4, 4)\n",
    "plt.gca().set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 Relevance Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 7.2.1 RVM for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M5JOLFClCYGAg/fv3Z+nSpRTKIK06JiaGmJgY7r333lvO7csYUQjWEDgmpTwB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\nBqIdgEbjiwT4K+loa+ZVwuXKwaFDULdMXTad2kS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1050ed9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_toy_data(n=10):\n",
    "    x = np.linspace(0, 1, n)\n",
    "    t = np.sin(2 * np.pi * x) + np.random.normal(scale=0.1, size=n)\n",
    "    return x, t\n",
    "\n",
    "x_train, y_train = create_toy_data(n=10)\n",
    "x = np.linspace(0, 1, 100)\n",
    "\n",
    "model = RelevanceVectorRegressor(RBF(np.array([1., 20.])))\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y, y_std = model.predict(x)\n",
    "\n",
    "plt.scatter(x_train, y_train, facecolor=\"none\", edgecolor=\"g\", label=\"training\")\n",
    "plt.scatter(model.X.ravel(), model.t, s=100, facecolor=\"none\", edgecolor=\"b\", label=\"relevance vector\")\n",
    "plt.plot(x, y, color=\"r\", label=\"predict mean\")\n",
    "plt.fill_between(x, y - y_std, y + y_std, color=\"pink\", alpha=0.2, label=\"predict std.\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2.3 RVM for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DVBTfzeVyYzKLHLnUnE4XFstFXjYvoAmYa4D9DaTtRZVXzf4KYObaSzvT9GA8z/6+nzG3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104476240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_toy_data():\n",
    "    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\n",
    "    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\n",
    "    x = np.concatenate([x0, x1])\n",
    "    y = np.concatenate([np.zeros(50), np.ones(50)]).astype(np.int)\n",
    "    return x, y\n",
    "\n",
    "x_train, y_train = create_toy_data()\n",
    "\n",
    "model = RelevanceVectorClassifier(RBF(np.array([1., 0.5, 0.5])))\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\n",
    "x = np.array([x0, x1]).reshape(2, -1).T\n",
    "plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\"x\")\n",
    "plt.scatter(model.X[:, 0], model.X[:, 1], s=100, facecolor=\"none\", edgecolor=\"g\")\n",
    "plt.contourf(x0, x1, model.predict_proba(x).reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)\n",
    "plt.colorbar()\n",
    "plt.xlim(-4, 4)\n",
    "plt.ylim(-4, 4)\n",
    "plt.gca().set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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